Early evidence from China suggested that blood groups may be involved in susceptibility to COVID-19. Several subsequent studies reported controversial results.We conducted a retrospective matched case-control study that aims to investigate the association between blood groups and the risk and/or severity of COVID-19.We compared the blood groups distribution of 474 patients admitted to the hospital for COVID-19 between March 2020 and March 2021, to that of a positive control group of outpatients infected with COVID-19 and matched them for sex and age, as well as to the distribution in the general population. Three hundred and eighteen HC + pairs with available blood group information were matched. The proportion of group A Rh+ in hospitalized patients (HC+) was 39.9% (CI 35.2%-44.7%), compared to 44.8% (CI 39.8%-49.9%) and 32.3% in the positive outpatient controls (C+) and the general population (C−), respectively. Both COVID-19-positive groups (HC+ and C+) had significantly higher proportions of group A Rh+ compared to the general population (p = 0.0019 and p < 0.001, respectively), indicating that group A Rh+ increases susceptibility to COVID-19. Although blood group A Rh+ was more frequent in the outpatients C+ compared to the hospitalized group HC+, the association did not reach statistical significance, indicating that blood group A Rh+ is not associated with severity. There was no significant relationship between COVID-19 and other blood groups. Our findings indicate that blood group A Rh+ increases the susceptibility for COVID-19 but is not associated with higher disease severity.

IntroductionAdult spinal deformity (ASD) is classically evaluated by health-related quality of life (HRQoL) questionnaires and static radiographic spino-pelvic and global alignment parameters. Recently, 3D movement analysis (3DMA) was used for functional assessment of ASD to objectively quantify patient's independence during daily life activities. The aim of this study was to determine the role of both static and functional assessments in the prediction of HRQoL outcomes using machine learning methods.MethodsASD patients and controls underwent full-body biplanar low-dose x-rays with 3D reconstruction of skeletal segment as well as 3DMA of gait and filled HRQoL questionnaires: SF-36 physical and mental components (PCS&amp;MCS), Oswestry Disability Index (ODI), Beck's Depression Inventory (BDI), and visual analog scale (VAS) for pain. A random forest machine learning (ML) model was used to predict HRQoL outcomes based on three simulations: (1) radiographic, (2) kinematic, (3) both radiographic and kinematic parameters. Accuracy of prediction and RMSE of the model were evaluated using 10-fold cross validation in each simulation and compared between simulations. The model was also used to investigate the possibility of predicting HRQoL outcomes in ASD after treatment.ResultsIn total, 173 primary ASD and 57 controls were enrolled; 30 ASD were followed-up after surgical or medical treatment. The first ML simulation had a median accuracy of 83.4%. The second simulation had a median accuracy of 84.7%. The third simulation had a median accuracy of 87%. Simulations 2 and 3 had comparable accuracies of prediction for all HRQoL outcomes and higher predictions compared to Simulation 1 (i.e., accuracy for PCS = 85 ± 5 vs. 88.4 ± 4 and 89.7% ± 4%, for MCS = 83.7 ± 8.3 vs. 86.3 ± 5.6 and 87.7% ± 6.8% for simulations 1, 2 and 3 resp., p &lt; 0.05). Similar results were reported when the 3 simulations were tested on ASD after treatment.DiscussionThis study showed that kinematic parameters can better predict HRQoL outcomes than stand-alone classical radiographic parameters, not only for physical but also for mental scores. Moreover, 3DMA was shown to be a good predictive of HRQoL outcomes for ASD follow-up after medical or surgical treatment. Thus, the assessment of ASD patients should no longer rely on radiographs alone but on movement analysis as well.

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